Human-level concept learning through probabilistic program induction

  title={Human-level concept learning through probabilistic program induction},
  author={Brenden M. Lake and Ruslan R. Salakhutdinov and Joshua B. Tenenbaum},
People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets… CONTINUE READING

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Foundations Trends Comput

  • D. Mumford
  • Graphics Vision
  • 2006

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